Crypto-LLM: Two-Stage Language Model Pre-training with Ciphered and Natural Language Data

AbstractAs the adoption of large language models (LLMs) continues to grow, the risk of sensitive data leakage from their training datasets has become a critical concern. This study proposes a novel method for encrypting training data using a polyalphabetic substitution cipher. This approach prevents the model from learning sensitive information while allowing it to capture abstract linguistic patterns. We pre-trained a Llama 3 model (551M parameters) using approximately 7.5 billion tokens of encrypted data and subsequently conducted continual pre-training with another 2.5 billion tokens of plaintext data. The effectiveness of the model was evaluated by comparing its downstream task performance with a model trained solely on plaintext data. In addition, we evaluated the risk of sensitive data leakage through name reconstruction, true-prefix and data extraction attacks. These results demonstrate the potential of our approach to balance data security with model performance.